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Part 1: Document Description
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Citation |
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Title: |
Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code] |
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Identification Number: |
doi:10.11588/data/ERDJDI |
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Distributor: |
heiDATA |
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Date of Distribution: |
2019-10-07 |
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Version: |
1 |
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Bibliographic Citation: |
Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1 |
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Citation |
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Title: |
Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code] |
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Identification Number: |
doi:10.11588/data/ERDJDI |
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Authoring Entity: |
Marasović, Ana (Department of Computational Linguistics, Heidelberg University, Germany) |
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Date of Production: |
2016 |
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Distributor: |
heiDATA |
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Access Authority: |
Marasović, Ana |
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Holdings Information: |
https://doi.org/10.11588/data/ERDJDI |
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Study Scope |
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Keywords: |
Arts and Humanities, Computer and Information Science, Modal sense classification (MSC), Word Sense Disambiguation, modal verb, word embedding, semantic feature |
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Topic Classification: |
semantic modeling |
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Abstract: |
<p><strong>Abstract</strong></p> <p>Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We bench-mark the CNN on a standard WSD task,where it compares favorably to models using sense-disambiguated target vectors. </p> <p>(Marasović and Frank, 2016)</p> |
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Kind of Data: |
program source code, python scripts |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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<p><strong>TensorFlow</strong></p> <p>Version: r0.12</p> <p>Link to the <a href="https://github.com/tensorflow/tensorflow/tree/r0.12">https://github.com/tensorflow/tensorflow/tree/r0.12</a></p> <p>License: <a href="https://spdx.org/licenses/Apache-2.0.html">Apache License 2.0</a></p> |
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Related Studies |
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<p><strong>The MSC Data Set:</strong><br />Link to the dataset: <a href="https://doi.org/10.11588/data/JEESIQ">https://doi.org/10.11588/data/JEESIQ</a> (heiDATA)</p> |
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Related Publications |
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Citation |
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Title: |
<p>Marasović, A. and Frank, A. (2016). Multilingual modal sense classification using a convolutional neural network. In <em>Proceedings of the 1st Workshop on Representation Learning for NLP</em>, pages 111–120, August 11, 2016, Berlin, Germany. Association for Computational Linguistics.</p> |
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Identification Number: |
10.18653/v1/W16-1613 |
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Bibliographic Citation: |
<p>Marasović, A. and Frank, A. (2016). Multilingual modal sense classification using a convolutional neural network. In <em>Proceedings of the 1st Workshop on Representation Learning for NLP</em>, pages 111–120, August 11, 2016, Berlin, Germany. Association for Computational Linguistics.</p> |
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Label: |
modal-sense-classifcation.zip |
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Notes: |
application/zip |